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Kernel-Based Discriminant Techniques for Educational Placement

Author

Listed:
  • Miao-hsiang Lin
  • Su-yun Huang
  • Yuan-chin Chang

Abstract

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s discriminant analysis and kernel-based nonparametric discriminant analysis, are compared. The evaluation work is based on the leaving-one-out misclassification score. Results from the five school data sets and 500 bootstrap samples reveal that the kernel-based nonparametric approach with bandwidth selected by cross validation performs reasonably well. The authors regard kernel-based nonparametric procedures as desirable competitors to Fisher’s discriminant rule for handling problems of educational placement.

Suggested Citation

  • Miao-hsiang Lin & Su-yun Huang & Yuan-chin Chang, 2004. "Kernel-Based Discriminant Techniques for Educational Placement," Journal of Educational and Behavioral Statistics, , vol. 29(2), pages 219-240, June.
    • Handle: RePEc:sae:jedbes:v:29:y:2004:i:2:p:219-240
    DOI: 10.3102/10769986029002219
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